PSXV-9 Transgenerational epigenetic variance for production and reproduction traits in maternal-line pigs
Bibliographic record
Abstract
Abstract Modeling epigenetic factors impacting phenotypic expression of economically important traits has become a hot-topic in the field of animal breeding due to the variability in genetic expression caused by environmental stressors (e.g., heat stress). This variability may be due, in part, to in-utero epigenomic remodeling, which has been reported to be passed from parent to offspring. We aimed to estimate transgenerational epigenetic variance for various production and reproduction traits measured in a maternal-line pig population, using a Bayesian approach. The phenotypes for production [n = 10,862; i.e., weaning weight (WW), birth weight (BW) and ultrasound-backfat thickness (BF)] and reproduction [n = 5,235, i.e., number of piglets born alive (NBA) and total number of piglets born (TB)] traits from a purebred Landrace population were provided by Smithfield Premium Genetics (NC, USA). The pedigree information traced back to 10 generations. Single-trait genetic analyses were performed using mixed models that included additive genetic, common environmental, and epigenetic random effects. The Gibbs sampler algorithm based on Markov chain Monte Carlo was used to estimate the variance components. The epigenetic relationship matrix was constructed using a recursive parameter (λ) related to the transmissibility coefficient of epigenetic markers. A grid search approach was used to define the optimal λ value (λ values ranged from 0.1 to 0.5, with an interval of 0.1). The optimal λ value was determined based on the deviance information criterion, and it was used to estimate the additive and epigenetic variances. For instance, based on preliminary results, the optimal λ value estimated for TB was 0.3 with an additive genetic variance of 0.94 (0.19 PSD) and epigenetic variance of 0.67 (0.18 PSD). The additive genetic heritability was 0.076 (0.015 PSD) and the estimated epigenetic heritability was 0.053 (0.015 PSD). This preliminary result suggests that epigenetics contribute to the non-Mendelian variability in pigs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".